Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
Adaptive blind separation of convolutive mixtures of independent linear signals
Signal Processing - Special issue on blind source separation and multichannel deconvolution
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Blind Channel Equalization and Identification
Blind Channel Equalization and Identification
Globally convergent blind source separation based on a multiuser kurtosis maximization criterion
IEEE Transactions on Signal Processing
Stochastic blind equalization based on PDF fitting using Parzen estimator
IEEE Transactions on Signal Processing
Statistical reference criteria for adaptive signal processing indigital communications
IEEE Transactions on Signal Processing
A bayesian approach to blind separation of mixed discrete sources by gibbs sampling
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
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This paper deals with criteria for adaptive blind separation of discrete sources. The criteria are based on the estimation of the probability density function (pdf) of the recovered signal using a parametric model and the divergence of Kullback-Leibler to measure the similarities between the involved signals. Two strategies that guarantee the recovering of all sources are employed: the first one introduces a penalty when the sources are correlated and the second one constrains the filtering to an orthogonal global system response. Simulations are carried out to evaluate the performance of the criteria compared with existing blind methods in typical multi-user environments such as spatial and space-time processing.